Archive for September, 2009

PRN 24 and PRN 25

PRN 24 (SVN 24) was recently set unhealthy due to some unusual problem. According to the GPS Operations Center:

An undisclosed user has reported an issue and 2 SOPS is switching SVN24
(PRN24) to test and turning SVN25 (PRN25) to operational while 2 SOPS investigates the situation. Constellation will remain optimized and there will be no impact to DOP.

So, not sure what the undisclosed user is seeing from PRN 24, has anyone else seen anything funny there? Also, restoring PRN 25 is interesting - is this to cover a DOP spike created by removing PRN 24? PRN 24 and 25 have completely different orbital positions (different planes even) as seen here. PRN 24 is red, PRN 25 is yellow:

A quick PDOP coverage calculation shows that there isn't much difference with PRN 25 in our out.

Also, PRN 24 was set unhealthy using a general NANU. What's up with that? If this means nothing to you, not to worry - for those of us who track this kind of stuff - ouch! This breaks established processes and causes all sorts of automated process chaos. We can do better.

Major speed improvement in AGI Components Release 2009R5

With the release of AGI Components 2009 R5 in September 2009, a big decrease in calculation time was introduced. This graph shows the computation time for various tests using the Navigation Accuracy Library. Notice the log scale for the Y-Axis. Results for 2009R4 are missing, but the results for 2009R5 are clearly a big step in the right direction. This is the fastest the components have run to date. Nice!

Newest, last IIR PRN 5, Poor Performer

So far, the newest GPS Satellite, PRN 5 (SVN 50) is no Einstein. The SIS errors are higher than almost all other satellites, as seen on this graph:

The clock seems to look ok, but looking at the ephemeris error (along with PRN 8 and PRN 27, the two worst performers), something just doesn't seem right:

It's not in Eclipse season, the minimum Sun/Earth angle is about 55 degrees:

Here's a VDF file for the scenario I grabbed the picture above from. Be sure to download the free AGI Viewer to animate and view PRN5 using the 3D globe.

There was a maintenance outage on PRN 5 recently (see the missing data on the last day on the graph above), let's wait and see if that has helped the ephemeris errors any.

Miscellaneous

I've always had a good sense of direction and because of that, I have yet to buy a 'GPS' for personal use. I usually can figure out where I'm going. Recently I was on a business trip to somewhere I hadn't been before (big metropolitan) and I decided to get a GPS in the rental car. I was on a short timeline and I didn't want to risk getting lost and missing my appointment. The unit I got locked on quickly and showed me the streets I would turn on, even talked to me. The talking was a bit late, but the arrows on the map helped make up for that. All in all it wasn't too bad - especially since they have other features like helping you find food, etc. It sure was a lot more helpful than the guy behind the desk at the hotel.

Later on, in a taxi ride, the taxi driver had another type of GPS on the dash - this one had all the same features, except it allowed you to download voices from the Internet. This is interesting. She played Darth Vader for awhile, then she turned on Kenny from South Park. I enjoyed hearing him tell the taxi driver to turn right..into the Johnson's car. Now THAT's worth buying one for.

I know, it's been a long time coming. Last February, I wrote a Nog on predicting GPS navigation errors in the long-term - over days and weeks. In this Nog, I'll cover predicting short term navigation errors, which is a little more tricky believe it or not. This is because for long-term errors, we can use statistics to predict the general behavior of GPS clocks and ephemeris, distilling that down into a statistical position error prediction. That type of prediction results in an error covariance, an error ellipsoid around the true position. For the short term (several hours), we have access to the latest clock and ephemeris errors and by using them we can create a predicted error vector, which is a better thing to have. The difference between an error ellipsoid and an error vector can be explained by example. Suppose you lose your car keys. Having an error ellipsoid may tell you that they are in your house somewhere, not too bad of a search, but you have to search the entire house. If you have an error vector, it would tell you that they are under last weeks mail in the kitchen junk drawer - much better information! A lot less searching. In the navigation world, and error ellipsoid tells you the treasure is in the general area, but an error vector points to the giant X on the map.

So, now that we have a basic understanding of the types of errors, let's look at how we might use the data we already have (in a PAF file) to predict error vectors for several hours. If you're not sure how a PAF file leads to a navigation error assessment, be sure to catch up withtheseNogs.